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Introducing ProvableWorldModel: the first provable JEPA world model. A real pretrained world model runs, and anyone can verify, on a laptop CPU, or on a mobile phone, that the committed model ran exactly as claimed, in milliseconds. The whole industry spent this week asking one question about Anthropic's Fable...

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abdel

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15,377 views • 2 months ago •via X (Twitter)

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VLA-JEPA just dropped in LeRobot 🤖 What makes this model special is that it does not just learn what action to take from a given observation, it also leverages a JEPA world model to learn action-relevant dynamics. During training, the VLA leverages V-JEPA2 by conditioning its predictor. This clever trick adds a world modeling objective to the training, which also allows pretraining on human videos. At inference, the world model is dropped entirely, keeping only a standard VLA architecture: Qwen backbone and action head. The demo here was only fine-tuned on 13 examples, showing great pretraining capability and running in real time on NVIDIA Robotics DGX Spark! VLA-JEPA is the first world model to be ported to LeRobot, and I feel like it won't be the last 🚀 Thomas Wolf clem 🤗
0:35

VLA-JEPA just dropped in LeRobot 🤖 What makes this model special is that it does not just learn what action to take from a given observation, it also leverages a JEPA world model to learn action-relevant dynamics. During training, the VLA leverages V-JEPA2 by conditioning its predictor. This clever trick adds a world modeling objective to the training, which also allows pretraining on human videos. At inference, the world model is dropped entirely, keeping only a standard VLA architecture: Qwen backbone and action head. The demo here was only fine-tuned on 13 examples, showing great pretraining capability and running in real time on NVIDIA Robotics DGX Spark! VLA-JEPA is the first world model to be ported to LeRobot, and I feel like it won't be the last 🚀 Thomas Wolf clem 🤗

LeRobot

319,779 views • 2 months ago

I am stocked to announce that I won the OpenAI Developers Codex x Mollie Hacka Worldwide Hackathon in Paris. 60+ builders, every one of us working solo, one day to ship. I built mine around a single question: who gets to own intelligence? The default answer is scary. You hand your data to a handful of labs, they train the model, they own it, and you rent back a thin slice of what your own data made possible. That is the bargain on the table today. I do not accept it. So I built Lensemble: a Tapestry like distributed training platform for JEPA based World Models. What does it enable: World Models that a community improves together, keeps sovereign, and co-owns. Two bets sit underneath it. First, the paradigm. Language models predict the next token. Powerful for text, a dead end for the physical world. A robot does not need to autocomplete sentences, it needs to predict what happens next in the world. That is what JEPA does: it learns by predicting representations instead of pixels or tokens. I am convinced world models are the most underrated paradigm in AI right now, and the closest thing we have to a ChatGPT moment for robotics. Second, the politics. Your raw trajectories never leave your machine. Each participant trains locally against a shared protocol and ships only an update, never the data. A federated round folds those updates into one shared world model, a LeWorldModel based model, and the gain is measured, not claimed: a 12k-parameter adapter on a frozen backbone, held-out prediction error down about 12 percent, the model measurably less surprised by the world. Then the upside is split by contribution weight, so the people who improved the model own a share of what it earns. This is the thesis behind Project Tapestry, the AI Alliance and Yann LeCun's push for federated, sovereign frontier AI, carried into world models and robotics. Call it Tapestry for the physical world. All of it built solo, in a single day, with Codex as my pair the whole way. Thank you to OpenAI Codex and Mollie for backing builders who ship real things, and to Boris and the organizing crew for the room and the standard you set. Intelligence the world improves, and the world owns. That is the future I want for my kids, and the one I will keep building.
1:13

I am stocked to announce that I won the OpenAI Developers Codex x Mollie Hacka Worldwide Hackathon in Paris. 60+ builders, every one of us working solo, one day to ship. I built mine around a single question: who gets to own intelligence? The default answer is scary. You hand your data to a handful of labs, they train the model, they own it, and you rent back a thin slice of what your own data made possible. That is the bargain on the table today. I do not accept it. So I built Lensemble: a Tapestry like distributed training platform for JEPA based World Models. What does it enable: World Models that a community improves together, keeps sovereign, and co-owns. Two bets sit underneath it. First, the paradigm. Language models predict the next token. Powerful for text, a dead end for the physical world. A robot does not need to autocomplete sentences, it needs to predict what happens next in the world. That is what JEPA does: it learns by predicting representations instead of pixels or tokens. I am convinced world models are the most underrated paradigm in AI right now, and the closest thing we have to a ChatGPT moment for robotics. Second, the politics. Your raw trajectories never leave your machine. Each participant trains locally against a shared protocol and ships only an update, never the data. A federated round folds those updates into one shared world model, a LeWorldModel based model, and the gain is measured, not claimed: a 12k-parameter adapter on a frozen backbone, held-out prediction error down about 12 percent, the model measurably less surprised by the world. Then the upside is split by contribution weight, so the people who improved the model own a share of what it earns. This is the thesis behind Project Tapestry, the AI Alliance and Yann LeCun's push for federated, sovereign frontier AI, carried into world models and robotics. Call it Tapestry for the physical world. All of it built solo, in a single day, with Codex as my pair the whole way. Thank you to OpenAI Codex and Mollie for backing builders who ship real things, and to Boris and the organizing crew for the room and the standard you set. Intelligence the world improves, and the world owns. That is the future I want for my kids, and the one I will keep building.

abdel

19,997 views • 1 month ago

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.
1:31

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,641 views • 2 months ago

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.
2:28

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.

Dustin

39,109 views • 4 months ago

Roboflow charges you for models you have to verify yourself. Introducing Score Studio: the decentralised alternative where every model arrives already proven and ready to work in real world conditions. Every model on the platform is battle-tested through SN44's validation mechanism before you ever touch it. Miners compete, validators score, only the winners survive. You get the output of a permanent open competition, at a fraction of the price. Datasets, annotation, training, deployment. All of it cheaper. All of it validated, accurate and efficient. Need a model that doesn't exist yet? Launch your own model inquiry from the Studio and put the network to work on it. Pay in USD or SN44, straight at subnet level. We're putting the final touches on it now.
2:14

Roboflow charges you for models you have to verify yourself. Introducing Score Studio: the decentralised alternative where every model arrives already proven and ready to work in real world conditions. Every model on the platform is battle-tested through SN44's validation mechanism before you ever touch it. Miners compete, validators score, only the winners survive. You get the output of a permanent open competition, at a fraction of the price. Datasets, annotation, training, deployment. All of it cheaper. All of it validated, accurate and efficient. Need a model that doesn't exist yet? Launch your own model inquiry from the Studio and put the network to work on it. Pay in USD or SN44, straight at subnet level. We're putting the final touches on it now.

Score - Subnet 44

33,210 views • 7 days ago

Leo Nightly now runs GLM-5 inside NEAR AI's Trusted Execution Environments, upgrading from DeepSeek V3.1. GLM-5 is currently one of the best open weight models from that leads every other open model on reasoning, coding, and agentic benchmarks. It's running inside Nvidia-backed TEEs via NEAR AI, which means your conversations are processed in a hardware-secured enclave where no one, not Brave, not NEAR, not anyone, can see your prompts or responses. Every computation is cryptographically signed so you can verify the model running is actually the model you selected. This is what we mean by moving from "trust me bro" to "trust but verify." Most AI assistants ask you to take their privacy claims on faith. Leo with NEAR AI TEEs gives you cryptographic proof. Update to the latest Nightly build and select GLM-5 with the "Verifiably Private with NEAR AI TEE" label in Leo to try it.
0:51

Leo Nightly now runs GLM-5 inside NEAR AI's Trusted Execution Environments, upgrading from DeepSeek V3.1. GLM-5 is currently one of the best open weight models from that leads every other open model on reasoning, coding, and agentic benchmarks. It's running inside Nvidia-backed TEEs via NEAR AI, which means your conversations are processed in a hardware-secured enclave where no one, not Brave, not NEAR, not anyone, can see your prompts or responses. Every computation is cryptographically signed so you can verify the model running is actually the model you selected. This is what we mean by moving from "trust me bro" to "trust but verify." Most AI assistants ask you to take their privacy claims on faith. Leo with NEAR AI TEEs gives you cryptographic proof. Update to the latest Nightly build and select GLM-5 with the "Verifiably Private with NEAR AI TEE" label in Leo to try it.

Brave Nightly

52,374 views • 5 months ago

We are glad to announce that we have full STARK compatibility between Stone and Lambdaworks Starknet (Privacy Arc) Platinum Prover. We’re working on adding the CairoVM constraints, the builtins and layouts. You can generate a proof with Lambda Stark Platinum and verify it with Stone following the instructions here: The 3 main objectives to achieve against the alternatives are: 1. the prover and verifier should be easy to run. one command to prove any cairo code. the prover internally calls the vm first to generate the trace, the user shouldn’t do anything but run one command. one command to verify it locally with the stone verifier. we will also add a command to verify it with the l1 contract in ethereum mainnet or testnets. compatibility and support of all the builtins are being worked on. we will be updating the community in the upcoming weeks. for us it’s very important that anybody can test and play with our code. 2. performance. we are already 10 times faster than the stone prover. we believe we can be almost another order of magnitude faster. 3. code architecture and organization should be top notch. the codebase is pretty small on purpose. we are documenting everything we are doing. we are leveraging all the work done in lambdaworks for multiple other provers. this let us easily iterate, play and change any part of the prover. we can test and propose new ideas thanks to this. from our point or view this is crucial and a big improvement over what exists. we have multiple ideas on how to change the protocol. And here's a demo! Don't trust, verify.
0:58

We are glad to announce that we have full STARK compatibility between Stone and Lambdaworks Starknet (Privacy Arc) Platinum Prover. We’re working on adding the CairoVM constraints, the builtins and layouts. You can generate a proof with Lambda Stark Platinum and verify it with Stone following the instructions here: The 3 main objectives to achieve against the alternatives are: 1. the prover and verifier should be easy to run. one command to prove any cairo code. the prover internally calls the vm first to generate the trace, the user shouldn’t do anything but run one command. one command to verify it locally with the stone verifier. we will also add a command to verify it with the l1 contract in ethereum mainnet or testnets. compatibility and support of all the builtins are being worked on. we will be updating the community in the upcoming weeks. for us it’s very important that anybody can test and play with our code. 2. performance. we are already 10 times faster than the stone prover. we believe we can be almost another order of magnitude faster. 3. code architecture and organization should be top notch. the codebase is pretty small on purpose. we are documenting everything we are doing. we are leveraging all the work done in lambdaworks for multiple other provers. this let us easily iterate, play and change any part of the prover. we can test and propose new ideas thanks to this. from our point or view this is crucial and a big improvement over what exists. we have multiple ideas on how to change the protocol. And here's a demo! Don't trust, verify.

Fede’s intern 🥊

16,713 views • 2 years ago

Choose a model (any model) and build your application with it. Do not spend time swapping models early on. Do not try to optimize before you have a working system. This is one of the first recommendations I make to every new team I consult with. Eventually, it will be time to optimize the model. • You may need a cheaper model • You may need a faster model • You might need a smarter model Good luck if you stitched together 12 different APIs and SDKs from 7 different vendors. Over half of the companies I consult for run on Microsoft software and have access to Microsoft Foundry. Microsoft Foundry is a complete agentic ecosystem. If you're in that world and building AI applications, Microsoft Foundry is where everything lives: • Models (largest selection in the market) • Agentic SDK (Python, C#, JavaScript/TypeScript) • Tools • Evaluations • Monitoring They are fully integrated with GitHub and Visual Studio Code. The best part: Their agentic platform is fully agnostic of the models you use. You can integrate with any model using the same OpenAI-style API. Swapping one model for another takes 1 second.
3:49

Choose a model (any model) and build your application with it. Do not spend time swapping models early on. Do not try to optimize before you have a working system. This is one of the first recommendations I make to every new team I consult with. Eventually, it will be time to optimize the model. • You may need a cheaper model • You may need a faster model • You might need a smarter model Good luck if you stitched together 12 different APIs and SDKs from 7 different vendors. Over half of the companies I consult for run on Microsoft software and have access to Microsoft Foundry. Microsoft Foundry is a complete agentic ecosystem. If you're in that world and building AI applications, Microsoft Foundry is where everything lives: • Models (largest selection in the market) • Agentic SDK (Python, C#, JavaScript/TypeScript) • Tools • Evaluations • Monitoring They are fully integrated with GitHub and Visual Studio Code. The best part: Their agentic platform is fully agnostic of the models you use. You can integrate with any model using the same OpenAI-style API. Swapping one model for another takes 1 second.

Santiago

12,014 views • 5 months ago

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.
3:19

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 views • 1 month ago

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.
2:51

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Vikram M

12,078 views • 24 days ago

“What if the most important AI infrastructure isn’t the biggest model — but the one you can actually trust?” Sachi Miyasaki sits down with Karan Sirdesai Karan, Founder of Mira, to explore how Mira is approaching decentralized AI from a more focused angle: making machine intelligence reliable enough for real-world use. They discuss why Mira is not trying to become a full-stack AI protocol, how its approach differs from networks like Bittensor, Ritual, and Sahara, and why focusing on trust and verification could make Mira a stronger fit inside the wider AI infrastructure stack. The episode also breaks down Mira’s reliability architecture, from its core verification layer to node-level consensus, and why the next evolution of AI APIs may not be about simply accessing more models, but about knowing whether the answers you receive can actually be trusted.
2:22

“What if the most important AI infrastructure isn’t the biggest model — but the one you can actually trust?” Sachi Miyasaki sits down with Karan Sirdesai Karan, Founder of Mira, to explore how Mira is approaching decentralized AI from a more focused angle: making machine intelligence reliable enough for real-world use. They discuss why Mira is not trying to become a full-stack AI protocol, how its approach differs from networks like Bittensor, Ritual, and Sahara, and why focusing on trust and verification could make Mira a stronger fit inside the wider AI infrastructure stack. The episode also breaks down Mira’s reliability architecture, from its core verification layer to node-level consensus, and why the next evolution of AI APIs may not be about simply accessing more models, but about knowing whether the answers you receive can actually be trusted.

BeaconLayer Podcast

79,073 views • 29 days ago

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.
16:27

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

abdel

17,001 views • 3 months ago

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.
1:45

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.

Dustin

121,810 views • 1 month ago

Satya Nadella was asked what Microsoft does when its AI revenue 10x's from $13B to $130B. He refused to answer in revenue - and named the only AGI benchmark he says counts "The first thing we have to observe is GDP growth. There's only one governor in all of this" "This is where a little bit of, we get ahead of ourselves with all this AGI hype" "The developed world is what, 2% growth? And if you adjust for inflation, it's zero. We have a real growth challenge" "When we say this is like the Industrial Revolution - that type of growth means to me 10%, 7%, developed world inflation adjusted, growing at 5%. That's the real marker" "The big winners here are not going to be tech companies. The winners are going to be the broader industry that uses this commodity" "Us self-claiming some AGI milestone - that's just nonsensical benchmark hacking to me. The real benchmark is the world growing at 10%" The CEO with one of the largest AI infrastructure bets on earth just told you to ignore every model benchmark and watch a single number: world GDP. By that test, nothing has happened yet. Developed-world growth is flat, and no lab's press release moves it. Real AGI shows up as the economy compounding at Industrial Revolution rates - or it isn't real.
1:51

Satya Nadella was asked what Microsoft does when its AI revenue 10x's from $13B to $130B. He refused to answer in revenue - and named the only AGI benchmark he says counts "The first thing we have to observe is GDP growth. There's only one governor in all of this" "This is where a little bit of, we get ahead of ourselves with all this AGI hype" "The developed world is what, 2% growth? And if you adjust for inflation, it's zero. We have a real growth challenge" "When we say this is like the Industrial Revolution - that type of growth means to me 10%, 7%, developed world inflation adjusted, growing at 5%. That's the real marker" "The big winners here are not going to be tech companies. The winners are going to be the broader industry that uses this commodity" "Us self-claiming some AGI milestone - that's just nonsensical benchmark hacking to me. The real benchmark is the world growing at 10%" The CEO with one of the largest AI infrastructure bets on earth just told you to ignore every model benchmark and watch a single number: world GDP. By that test, nothing has happened yet. Developed-world growth is flat, and no lab's press release moves it. Real AGI shows up as the economy compounding at Industrial Revolution rates - or it isn't real.

Karl Mehta

82,403 views • 1 month ago

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.
2:13

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

Ricardo

43,436 views • 12 days ago

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.
0:22

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.

Fireside Alpha

10,037 views • 1 month ago

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.
2:52

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 views • 2 years ago

See how our Director of Growth and AI Partnerships ravidilse.eth walks you through the Proof of Uniqueness (PoU) process in 4 steps: Securing a Sentient Airdrop with PoU from Billions Step 1: Create Account Step 2: Check Eligibility Step 3: Verify PoU Step 4: Add Wallet Address You are not expected to download any software or install any application. The verification happens on your device. Within a few seconds, you are able to complete the verification process. Once completed, you will have the liveliness and uniqueness credential in your wallet. With a click of the "Verify" button, you can then generate a zero knowledge proof that can be shared with the verifier. The verifier can be Sentient or a third party like Clique. Note: No user data is being stored by Billions. Once the verification is done, you are eligible for the airdrop. This is where Sentient ensures that the treasury tokens are not being distributed to bot farmers - instead they are distributed to real, unique humans like you.
1:28

See how our Director of Growth and AI Partnerships ravidilse.eth walks you through the Proof of Uniqueness (PoU) process in 4 steps: Securing a Sentient Airdrop with PoU from Billions Step 1: Create Account Step 2: Check Eligibility Step 3: Verify PoU Step 4: Add Wallet Address You are not expected to download any software or install any application. The verification happens on your device. Within a few seconds, you are able to complete the verification process. Once completed, you will have the liveliness and uniqueness credential in your wallet. With a click of the "Verify" button, you can then generate a zero knowledge proof that can be shared with the verifier. The verifier can be Sentient or a third party like Clique. Note: No user data is being stored by Billions. Once the verification is done, you are eligible for the airdrop. This is where Sentient ensures that the treasury tokens are not being distributed to bot farmers - instead they are distributed to real, unique humans like you.

Billions

21,620 views • 7 months ago

Karpathy said something you'll regret ignoring: "We have to keep the AI on the leash. I'm still the bottleneck. I have to make sure this thing isn't introducing bugs and that there's no security issues." He said it at YC talk last year, when the worry was reliability. The models hallucinated and made mistakes no human would, so the leash implied keeping yourself in the loop and checking the output before trusting it. The models are far better now, and the line still holds, for a reason he was not focused on back then. Even a model that writes flawless code today still has no idea who is allowed to run it. Correctness and authorization are different problems, and only correctness improves as the model improves. A perfect agent still hands a tool where anyone can do anything, because permission was never part of the task. I actually tested this in practice with Claude Code. I asked it to build a small internal tool with a button that issues account credits. It worked first try, and running it locally, the credit applied the instant I clicked. Nothing decided who was allowed to click it. The agent wrote the right logic and displayed a success notification. It never checked whether the caller had the right, whether it should pause for a human, or whether anything was logged. And this is not a bug a smarter model can outgrow because the leash was never in the code. Identity, permissions, and audit live in the system that runs the app, not in what the agent generates. To solve this, I took the exact same bundle and hosted it on Retool. The credit write that fired silently on my laptop now stopped at an approval gate, resolved to a real identity through SSO, and landed in an audit log. I wrote none of it. The app inherited the entire boundary the moment it was deployed, and the video shows the before and after. You can try it yourself here: I also wrote a detailed breakdown of the whole thing in my recent article, and I worked with the team to put this together. It walks through the build, the exact moment the credit write went through on my laptop with nobody checking, and then what changed when the same app ran on Retool. It also covers why this is a property of the runtime and not something a better model fixes, which is why devs typically miss this. The article is quoted below.
0:38

Karpathy said something you'll regret ignoring: "We have to keep the AI on the leash. I'm still the bottleneck. I have to make sure this thing isn't introducing bugs and that there's no security issues." He said it at YC talk last year, when the worry was reliability. The models hallucinated and made mistakes no human would, so the leash implied keeping yourself in the loop and checking the output before trusting it. The models are far better now, and the line still holds, for a reason he was not focused on back then. Even a model that writes flawless code today still has no idea who is allowed to run it. Correctness and authorization are different problems, and only correctness improves as the model improves. A perfect agent still hands a tool where anyone can do anything, because permission was never part of the task. I actually tested this in practice with Claude Code. I asked it to build a small internal tool with a button that issues account credits. It worked first try, and running it locally, the credit applied the instant I clicked. Nothing decided who was allowed to click it. The agent wrote the right logic and displayed a success notification. It never checked whether the caller had the right, whether it should pause for a human, or whether anything was logged. And this is not a bug a smarter model can outgrow because the leash was never in the code. Identity, permissions, and audit live in the system that runs the app, not in what the agent generates. To solve this, I took the exact same bundle and hosted it on Retool. The credit write that fired silently on my laptop now stopped at an approval gate, resolved to a real identity through SSO, and landed in an audit log. I wrote none of it. The app inherited the entire boundary the moment it was deployed, and the video shows the before and after. You can try it yourself here: I also wrote a detailed breakdown of the whole thing in my recent article, and I worked with the team to put this together. It walks through the build, the exact moment the credit write went through on my laptop with nobody checking, and then what changed when the same app ran on Retool. It also covers why this is a property of the runtime and not something a better model fixes, which is why devs typically miss this. The article is quoted below.

Akshay 🚀

42,911 views • 1 month ago

Today we're launching Caution, a hosting platform for software you can't afford to have hacked. Deploy sensitive workloads to secure enclaves in minutes and give customers cryptographic proof that prod is running the exact release you approved, not an opaque binary you have to trust. Caution is built for payment processors, healthcare platforms, AI agents and inference, crypto infrastructure, and other sensitive systems. It removes the blind trust software runs on today: trust in the cloud host, in production admins, in CI/CD, and in the assumption that the code you reviewed is the code actually running. We spent years securing systems responsible for over $600B in assets and hit the same wall every time: teams couldn't prove what was running in production. Now that AI can find and exploit zero-days faster than anyone can patch, "trust us" isn't enough. Infrastructure has to stay secure even when it's compromised, and prove it. Verifiable compute becomes the default, like HTTPS did.
1:32

Today we're launching Caution, a hosting platform for software you can't afford to have hacked. Deploy sensitive workloads to secure enclaves in minutes and give customers cryptographic proof that prod is running the exact release you approved, not an opaque binary you have to trust. Caution is built for payment processors, healthcare platforms, AI agents and inference, crypto infrastructure, and other sensitive systems. It removes the blind trust software runs on today: trust in the cloud host, in production admins, in CI/CD, and in the assumption that the code you reviewed is the code actually running. We spent years securing systems responsible for over $600B in assets and hit the same wall every time: teams couldn't prove what was running in production. Now that AI can find and exploit zero-days faster than anyone can patch, "trust us" isn't enough. Infrastructure has to stay secure even when it's compromised, and prove it. Verifiable compute becomes the default, like HTTPS did.

Anton Livaja

484,864 views • 10 days ago